In a number of industries, three-dimensional (3D) models may be used to represent various objects. For example, any industry that uses or sells objects may need to understand how different objects interact or how they might appear together. The generation of such a 3D model may require that a system obtain an image of the surface of an object, which may be rendered or displayed as a two-dimensional image via 3D rendering or displayed as a three-dimensional image.
Various techniques exist for generating 3D models from objects. For example, a given object may be scanned from a number of different angles, and the scanned images can then be combined to generate the 3D image of the object. In another example, a 3D model may be created manually by an artist or drafter.
Often, when an object is being scanned, it is desirable to show near exact color and visual material properties to more accurately depict how the object looks in the real world. However, due to lighting and reflectiveness of a sample being scanned, some visual material properties, such as whether an item is metallic or has rough surfaces, can be hard to assess. For example, it is often difficult to assess visual material properties such as metalness or roughness. “Metalness” is a measure of how metallic something is. “Roughness” is a measure of how shiny or reflective something is. If something is very rough, it is not shiny at all. If something is not rough at all, then it has very sharp highlights (e.g., shiny).
In some conventional systems, default visual material properties are applied to every object, resulting in generated 3D models that look equally shiny. The resulting 3D models lack realism. In some other conventional systems, an artist may estimate various visual material properties for an object based on the captured image. However, this often results in inconsistencies and is error prone in general. Some conventional systems may use a device, such as a Gonioreflectometer, which directly captures the bidirectional reflectance (BRDF) of a material per pixel. Devices such as this take thousands of images of an object from every possible camera angle, and from every possible lighting direction. However, capturing the number of images of an object required by the device can be inefficient and costly.
Embodiments of the invention address these and other problems, individually and collectively.